class_docstr: Alternating Least Squares
  
  A Recommendation Model based off the algorithms described in the paper 'Collaborative
  Filtering for Implicit Feedback Datasets' with performance optimizations described in
  'Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative
  Filtering.'
  
  Parameters
  ----------
  factors : int, optional
      The number of latent factors to compute
  regularization : float, optional
      The regularization factor to use
  alpha : float, optional
      The weight to give to positive examples.
  dtype : data-type, optional
      Specifies whether to generate 64 bit or 32 bit floating point factors
  use_native : bool, optional
      Use native extensions to speed up model fitting
  use_cg : bool, optional
      Use a faster Conjugate Gradient solver to calculate factors
  iterations : int, optional
      The number of ALS iterations to use when fitting data
  calculate_training_loss : bool, optional
      Whether to log out the training loss at each iteration
  num_threads : int, optional
      The number of threads to use for fitting the model and batch recommend calls.
      Specifying 0 means to default to the number of cores on the machine.
  random_state : int, numpy.random.RandomState, np.random.Generator or None, optional
      The random state for seeding the initial item and user factors.
      Default is None.
  
  Attributes
  ----------
  item_factors : ndarray
      Array of latent factors for each item in the training set
  user_factors : ndarray
      Array of latent factors for each user in the training set
docstr: Provides explanations for why the item is liked by the user.
  
  Parameters
  ----------
  userid : int
      The userid to explain recommendations for
  user_items : csr_matrix
      Sparse matrix containing the liked items for the user
  itemid : int
      The itemid to explain recommendations for
  user_weights : ndarray, optional
      Precomputed Cholesky decomposition of the weighted user liked items.
      Useful for speeding up repeated calls to this function, this value
      is returned
  N : int, optional
      The number of liked items to show the contribution for
  
  Returns
  -------
  total_score : float
      The total predicted score for this user/item pair
  top_contributions : list
      A list of the top N (itemid, score) contributions for this user/item pair
  user_weights : ndarray
      A factorized representation of the user. Passing this in to
      future 'explain' calls will lead to noticeable speedups
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code: def explain(
      self, userid, user_items, itemid, user_weights=None, N=10
  ):
      """Provides explanations for why the item is liked by the user.
  
      Parameters
      ----------
      userid : int
          The userid to explain recommendations for
      user_items : csr_matrix
          Sparse matrix containing the liked items for the user
      itemid : int
          The itemid to explain recommendations for
      user_weights : ndarray, optional
          Precomputed Cholesky decomposition of the weighted user liked items.
          Useful for speeding up repeated calls to this function, this value
          is returned
      N : int, optional
          The number of liked items to show the contribution for
  
      Returns
      -------
      total_score : float
          The total predicted score for this user/item pair
      top_contributions : list
          A list of the top N (itemid, score) contributions for this user/item pair
      user_weights : ndarray
          A factorized representation of the user. Passing this in to
          future 'explain' calls will lead to noticeable speedups
      """
  
      user_items = check_csr(user_items)
      if self.alpha != 1.0:
          user_items = self.alpha * user_items
  
      # user_weights = Cholesky decomposition of Wu^-1
      # from section 5 of the paper CF for Implicit Feedback Datasets
      if user_weights is None:
          A, _ = user_linear_equation(
              self.item_factors,
              self.YtY,
              user_items,
              userid,
              self.regularization,
              self.factors,
          )
          user_weights = scipy.linalg.cho_factor(A)
      seed_item = self.item_factors[itemid]
  
      # weighted_item = y_i^t W_u
      weighted_item = scipy.linalg.cho_solve(user_weights, seed_item)
  
      total_score = 0.0
      h = []
      h_len = 0
      for other_itemid, confidence in nonzeros(user_items, userid):
          if confidence < 0:
              continue
  
          factor = self.item_factors[other_itemid]
          # s_u^ij = (y_i^t W^u) y_j
          score = weighted_item.dot(factor) * confidence
          total_score += score
          contribution = (score, other_itemid)
          if h_len < N:
              heapq.heappush(h, contribution)
              h_len += 1
          else:
              heapq.heappushpop(h, contribution)
  
      items = (heapq.heappop(h) for i in range(len(h)))
      top_contributions = list((i, s) for s, i in items)[::-1]
      return total_score, top_contributions, user_weights
  
imports: import functools
  import heapq
  import logging
  import time
  import numpy
  import scipy
  import scipy.sparse
  from tqdm.auto import tqdm
  from ..utils import check_blas_config, check_csr, check_random_state, nonzeros
  from .None import _als
  from .matrix_factorization_base import MatrixFactorizationBase
label_desc: Use image node_id 7 for calling the AlternatingLeastSquares obj's underlying member method code with example usage: obj.explain(userid, user_items, itemid, user_weights, N) and returns: total_score,  top_contributions,  user_weights